We use the idea of data augmentation task, mixup as a regularization [1]} task to improve our test set validation error (there by increasing accuracy) which would also help us to do better classification if we want to classify a new image that completely out of domain of our train set. We also do Bayes hyper-parameters sweep using wandb [2]} to find the best hyper-parameters for our model after applying this regularization task.
